Multiple views of ground data system
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ground software GDS↗
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Multiple views of ground data systems: based on Mars Reconnaissance Orbiter's GDS
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ground data systems↗
Multiple views of ground data systems: based on Mars Reconnaissance Orbiter's GDS
Explore the source record for details and available documents.
ground data systems↗
Validation of Cloud Properties From Multiple Satellites Using CALIOP Data
The NASA Langley Satellite ClOud and Radiative Property retrieval System (SatCORPS) is routinely applied to multispectral imagery from several geostationary and polar-orbiting imagers to retrieve cloud properties for weather and climate applications. Validation of the retrievals with independent datasets is continuously ongoing in order to understand differences caused by calibration, spatial resolution, viewing geometry, and other factors. The CALIOP instrument provides a decade of detailed cloud observations which can be used to evaluate passive imager retrievals of cloud boundaries, thermodynamic phase, cloud optical depth, and water path on a global scale. This paper focuses on comparisons of CALIOP retrievals to retrievals from MODIS, VIIRS, AVHRR, GOES, SEVIRI, and MTSAT. CALIOP is particularly skilled at detecting weakly-scattering cirrus clouds with optical depths less than approx. 0.5. These clouds are often undetected by passive imagers and the effect this has on the property retrievals is discussed.
Yost, Christopher R.↗
Applications of Satellite Data to Support Improvements in Irrigation and Groundwater Management in California
In agricultural regions around the world, threats to water supplies from drought and groundwater depletion are driving increased demand for tools to advance agricultural water use efficiency and support sustainable groundwater management. Satellite mapping of evapotranspiration (ET) from irrigated agricultural lands can provide agricultural producers and water resource managers with information that can be used to both optimize ag water use and improve estimates of groundwater withdrawals for irrigation. We describe the development of two remote sensing-based tools for ET mapping in California, including important lessons in terms of system design, partnership development, and transition to operations. For irrigation management, the integration of satellite data and surface sensor networks to provide timely delivery of information on crop water requirements can make irrigation scheduling more practical, convenient, and accurate.Developed through a partnership between NASA and the CA Department of Water Resources, the Satellite Irrigation Management Support (SIMS) framework integrates satellite data with information from agricultural weather networks to map crop canopy development and crop water requirements at the scale of individual fields. Information is distributed to agricultural producers and water managers via a web-based interface and web data services. SIMS also provides an API that facilitates integration with other irrigation decision support tools, such as CropManage and IrriQuest. Field trials using these integrated tools have shown that they can be used to sustain yields while improving water use efficiency and nutrient management. For sustainable groundwater management, the combination of satellite-derived estimates of ET and data on surface water deliveries for irrigation can increase the accuracy of estimates of groundwater pumping. We are developing an OpenET platform to facilitate access to ET data from multiple models and accelerate operational use of ET data in support of a range of water management applications, including implementation of the Sustainable Groundwater Management Act in CA. By providing a shared basis for decision making, we anticipate that the OpenET platform will accelerate implementation of solutions for sustainable groundwater management.
Satellite↗
Impact of Canadian Wildfires on Mid Atlantic’s Region Air Quality: An Analysis Using ASDC Data
Wildfires pose a growing concern in North America due to their harmful impacts on air quality and public health, with increased wildfire activity in recent years leading to widespread smoke plumes that can transcend borders. The exposure of New York City (NYC), the most populous city in North America, to Canadian wildfire smoke highlights the substantial implications for public health and urban environments. To better understand the impact of Canadian wildfires on air quality in NYC, satellite data from the NASA Atmospheric Science Data Center (ASDC) at Langley Research Center, along with ground-based measurements and atmospheric modeling results, are analyzed. We examine concentrations of atmospheric aerosols—particularly PM2.5 particulate matter originating from Canadian wildfires—their dispersion patterns, and the duration and intensity of smoke events impacting NYC. Data from multiple satellites, such as those from the Earth Polychromatic Imaging Camera (EPIC), are synergistically used to identify regions affected by wildfires and estimate aerosol loading. Ground-based measurements, including data from air quality monitoring stations, provide localized information for validation and calibration purposes. The findings of this study contribute to our understanding of the impact of Canadian wildfires on NYC's air quality and emphasize the importance of monitoring and prediction of transboundary smoke events using data synthesized from multiple sources, such as those provided by the ASDC. This information is crucial for policymakers, public health officials, and residents in affected areas to develop effective strategies for mitigating the health risks associated with wildfire smoke and improving air quality during wildfire seasons. The utilization of ASDC data in this research highlights the critical role of atmospheric remote sensing in addressing the challenges posed by wildfires and their consequences on regional scales.
Ingrid Garcia-Solera↗
Analyzing the Impact of Canadian Wildfires on Air Quality in the U.S. Mid-Atlantic: with Data and Tools from NASA’s Atmospheric Sciences Data Center
Wildfires pose a growing concern in North America due to their harmful impacts on air quality and public health, with increased wildfire activity in recent years leading to widespread smoke plumes that can transcend borders. The exposure of New York City (NYC), the most populous city in North America, to Canadian wildfire smoke highlights the substantial implications for public health and urban environments. To better understand the impact of Canadian wildfires on air quality in NYC, satellite data from the NASA Atmospheric Science Data Center (ASDC) at Langley Research Center, along with ground-based measurements and atmospheric modeling results, are analyzed. We examine concentrations of atmospheric aerosols—particularly PM2.5 particulate matter originating from Canadian wildfires—their dispersion patterns, and the duration and intensity of smoke events impacting NYC. Data from multiple satellites, such as those from the Earth Polychromatic Imaging Camera (EPIC), are synergistically used to identify regions affected by wildfires and estimate aerosol loading. Ground-based measurements, including data from air quality monitoring stations, provide localized information for validation and calibration purposes. The findings of this study contribute to our understanding of the impact of Canadian wildfires on NYC's air quality and emphasize the importance of monitoring and prediction of transboundary smoke events using data synthesized from multiple sources, such as those provided by the ASDC. This information is crucial for policymakers, public health officials, and residents in affected areas to develop effective strategies for mitigating the health risks associated with wildfire smoke and improving air quality during wildfire seasons. The utilization of ASDC data in this research highlights the critical role of atmospheric remote sensing in addressing the challenges posed by wildfires and their consequences on regional scales.
Ingrid Garcia-Solera↗
Multi-channel rotating optical interface for data transmission
Apparatus for transmitting multiple channels of data across a rotating interface, such as between an antenna that rotates with respect to a platform, is described. Features of the apparatus include: (1) light emitter elements and light detector elements located on the two bodies that rotate relative to each other; (2) a lens for focusing light from each emitter element onto a corresponding detector element; and (3) an image rotating means which is turned as one of the objects rotates, to derotate the images of the emitter elements that are to be focused on the detector elements.
Ivie, C. V.↗
A data management system for engineering and scientific computing
Data elements and relationship definition capabilities for this data management system are explicitly tailored to the needs of engineering and scientific computing. System design was based upon studies of data management problems currently being handled through explicit programming. The system-defined data element types include real scalar numbers, vectors, arrays and special classes of arrays such as sparse arrays and triangular arrays. The data model is hierarchical (tree structured). Multiple views of data are provided at two levels. Subschemas provide multiple structural views of the total data base and multiple mappings for individual record types are supported through the use of a REDEFINES capability. The data definition language and the data manipulation language are designed as extensions to FORTRAN. Examples of the coding of real problems taken from existing practice in the data definition language and the data manipulation language are given.
Elliot, L.↗
End-to-End Data System Architecture for the Space Station Biological Research Project
The Space Station Biological Research Project (SSBRP) Is developing hardware referred to as the "facility" for providing life sciences research capability on the International Space Station. This hardware includes several biological specimen habitats, habitat holding racks, a centrifuge and a glovebox. An SSBRP end to end data system architecture has been developed to allow command and control of the facility from the ground, either with crew assistance or autonomously. The data system will be capable of handling commands, sensor data, and video from multiple cameras. The data will traverse through several onboard and ground networks and processing entities including the SSBRP and Space Station onboard and ground data systems. A large number of onboard and ground (,entities of the data system are being developed by the Space Station Program, other NASA centers and the International Partners. The SSBRP part of the system which includes the habitats, holding racks, and the ground operations center, User Operations Facility (UOF) will be developed by a multitude of geographically distributed development organizations. The SSBRP has the responsibility to define the end to end data and communications systems to make the interfaces manageable and verifiable with multiple contractors with widely varying development constraints and schedules. This paper provides an overview of the SSBRP end-to-end data system. Specifically, it describes the hardware, software and functional interactions of individual systems, and interface requirements among various entities of the end-to-end system.
Mian, Arshad↗
Verification of static rotor-load measurement capabilities of the RSRA helicopter active-isolator system
The Rotor Systems Research Aircraft helicopter technology demonstration test bed incorporates an active isolator system which reduces the rotor vibrations that are transmitted to the airframe and allows the simultaneous measurement of all six forces and moments generated by the rotor. The first full system calibration was performed in 1983 to verify the system's static load measurement capabilities; the analysis of the data encompassed multiple linear regressions to determine calibration matrices for different data sets, and a hysteresis removal algorithm for the estimation of in-flight measurement errors. The results obtained indicate that the active isolator system meets most performance predictions.
Acree, C. W., Jr.↗
Autonomous image data reduction by analysis and interpretation
Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.
Eberlein, Susan↗
Autonomous image data reduction by analysis and interpretation
Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision-making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.
Eberlein, Susan↗
An Evaluation of Oceanographic Optical Instruments and Deployment Methodologies
The primary objective of the Sea-viewing, Wide Field-of-view Sensor (SeaWiFS) Project is to produce water- leaving radiances with an uncertainty of 5% in clear-water regions and chlorophyll a concentrations within +/- 35% over the range of 0.05-50 mg/cu m. Any global mission, like SeaWiFS, requires validation data be submitted from a wide variety of investigators which places a significant challenge on quantifying the total uncertainty associated with the in situ measurements, because each investigator follows slightly different practices when it comes to implementing all of the steps associated with collecting field data, even those with a prescribed set of protocols. This study uses data from multiple cruises to quantify the uncertainties associated with implementing data collection procedures while utilizing differing in-water optical instruments and deployment methods. A comprehensive approach is undertaken and includes: (1) the use of a portable light source and in-water intercomparisons to monitor the stability of the field radiometers, (2) alternative methods for acquiring reference measurements, and (3) different techniques for making in-water profiles. The only system to meet the 5% radiometric objective of the SeaWiFS Project was a free-fall profiler using (relatively inexpensive) modular components, although a more sophisticated (and comparatively expensive) profiler using integral components was very close and only 1% higher. A relatively inexpensive system deployed with a winch and crane was also close, but the ship shadow contamination problem increased the total uncertainty to approximately 6.5%.
Hooker, Stanford B.↗
An Oveview of TROPESS Data Products and Services at the NASA GES DISC
The TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project generates Earth System Data Records (ESDRs) of ozone, and other atmospheric constituents (CH4,CO, H2O, HDO, NH3, PAN and temperature) by processing data from multiple satellites through a common retrieval algorithm and ground data system. Satellite Level-1B input data used in generating the TROPESS L2 data products include CrIS NOAA-20 (JPSS-1), CrIS SNPP, AIRS Aqua, OMI Aura, and TROPOMI S5P. The common retrieval framework is known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) science data processing system (MUSES-SDPS). Several of the TROPESS data products are now available from the NASA Goddard Earth Sciences Data and Information Service Center (GES DISC) for users to download. In this presentation we provide an overview of the various TROPESS data products. These data products can be divided into the following Forward Stream types: Standard Products, Summary Products, and Full-Archival Products. Standard Products are for users that are doing full analysis with avenging kernel and covariance corresponding to retrieved vertical profiles. Summary products have a smaller file size and are more convenient for first-look and rapid analysis, include total and partial columns, as well as column averaging kernels. The Full-Archival Products will contain all information used in creating the data. TROPESS also creates Special Products, provided on an as-needed and as-available basis to support NASA field missions and individual-investigator requests over specific regions. Eventually, TROPESS will also produce and deliver a set of Reanalysis Stream products. Data at the GES DISC are being transitioned into the "Cloud". This will allow users with "Cloud” access to perform data analysis directly on the data without downloading the data to their system. Services, such as subsetting and data visualization, will also be provided for TROPESS data products at the GES DISC.
James Johnson↗
Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)
The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.
cloud computing↗
Automated attendance accounting system
An automated accounting system useful for applying data to a computer from any or all of a multiplicity of data terminals is disclosed. The system essentially includes a preselected number of data terminals which are each adapted to convert data words of decimal form to another form, i.e., binary, usable with the computer. Each data terminal may take the form of a keyboard unit having a number of depressable buttons or switches corresponding to selected data digits and/or function digits. A bank of data buffers, one of which is associated with each data terminal, is provided as a temporary storage. Data from the terminals is applied to the data buffers on a digit by digit basis for transfer via a multiplexer to the computer.
Chapman, C. P.↗
Analysis of Flowfields over Four-Engine DC-X Rockets
The objective of this study is to validate a computational methodology for the aerodynamic performance of an advanced conical launch vehicle configuration. The computational methodology is based on a three-dimensional, viscous flow, pressure-based computational fluid dynamics formulation. Both wind-tunnel and ascent flight-test data are used for validation. Emphasis is placed on multiple-engine power-on effects. Computational characterization of the base drag in the critical subsonic regime is the focus of the validation effort; until recently, almost no multiple-engine data existed for a conical launch vehicle configuration. Parametric studies using high-order difference schemes are performed for the cold-flow tests, whereas grid studies are conducted for the flight tests. The computed vehicle axial force coefficients, forebody, aftbody, and base surface pressures compare favorably with those of tests. The results demonstrate that with adequate grid density and proper distribution, a high-order difference scheme, finite rate afterburning kinetics to model the plume chemistry, and a suitable turbulence model to describe separated flows, plume/air mixing, and boundary layers, computational fluid dynamics is a tool that can be used to predict the low-speed aerodynamic performance for rocket design and operations.
Wang, Ten-See↗